While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization.
Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a ProGAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N = 30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results.
Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing.
[Keywords: brain-computer interfaces, electroencephalography (EEG), generative adversarial networks (GANs), image generation, attraction, personal preferences, individual differences]
…Thus, negative generated images were evaluated as highly attractive for other people, but not for the participant themselves. Taken together, the results suggest that the GBCI was highly accurate in generating personally attractive images (83.33%). They also show that while both negative and positive generated images were evaluated as highly attractive for the general population (respectively M = 4.43 and 4.90 on a scale of 1–5), only the positive generated images (M = 4.57) were evaluated as highly personally attractive.
Qualitative results: In semi-structured post-test interviews, participants were shown the generated images that were expected to be found attractive/ unattractive. Thematic analysis found predictions of positive attractiveness were experienced as accurate: There were no false positives (generated unattractive found personally attractive). The participants also expressed being pleased with results (eg. “Quite an ideal beauty for a male!”; “I would be really attracted to this!”; “Can I have a copy of this? It looks just like my girlfriend!”).